[guided]The target term has the sign that is most important in the argument, so we check it one pair of indices at a time. For fixed $i,j\in\{1,\dots,m\}$, define the two tangent vectors at the point $u(p)\in N$ by $a_{ij}:=du_p(e_i)\in T_{u(p)}N$ and $b_{ij}:=du_p(e_j)\in T_{u(p)}N$.
We need to determine the sign of
\begin{align*}
h_{u(p)}(R^N(a_{ij},b_{ij})b_{ij},a_{ij}).
\end{align*}
First suppose $a_{ij}$ and $b_{ij}$ are linearly independent. Then they span a $2$-plane in $T_{u(p)}N$, and the definition of sectional curvature gives
\begin{align*}
h_{u(p)}(R^N(a_{ij},b_{ij})b_{ij},a_{ij})
=
K_N(a_{ij}\wedge b_{ij})
\bigl(|a_{ij}|_h^2|b_{ij}|_h^2-h_{u(p)}(a_{ij},b_{ij})^2\bigr).
\end{align*}
The second factor is the Gram determinant of the two vectors. By the Cauchy-Schwarz inequality,
\begin{align*}
h_{u(p)}(a_{ij},b_{ij})^2\leq |a_{ij}|_h^2|b_{ij}|_h^2,
\end{align*}
and because the vectors are linearly independent, equality does not occur. Thus
\begin{align*}
|a_{ij}|_h^2|b_{ij}|_h^2-h_{u(p)}(a_{ij},b_{ij})^2>0.
\end{align*}
The curvature hypothesis says $K_N(a_{ij}\wedge b_{ij})\leq 0$, so the product is nonpositive:
\begin{align*}
h_{u(p)}(R^N(a_{ij},b_{ij})b_{ij},a_{ij})\leq 0.
\end{align*}
Now suppose $a_{ij}$ and $b_{ij}$ are linearly dependent. If one of the two vectors is zero, then $R^N(a_{ij},b_{ij})=0$ by bilinearity. If neither is zero, there is a scalar $\lambda\in\mathbb{R}$ such that $b_{ij}=\lambda a_{ij}$, and the alternating property of the curvature tensor in its first two arguments gives
\begin{align*}
R^N(a_{ij},b_{ij})
=
R^N(a_{ij},\lambda a_{ij})
=
\lambda R^N(a_{ij},a_{ij})
=
0.
\end{align*}
Therefore the same inequality holds in the dependent case:
\begin{align*}
h_{u(p)}(R^N(a_{ij},b_{ij})b_{ij},a_{ij})\leq 0.
\end{align*}
Since this estimate holds for every pair $(i,j)$, summing preserves the inequality:
\begin{align*}
\sum_{i,j=1}^{m}
h_{u(p)}
\bigl(
R^N(du_p(e_i),du_p(e_j))du_p(e_j),
du_p(e_i)
\bigr)
\leq 0.
\end{align*}
Multiplying by the minus sign appearing in the Bochner formula reverses the sign and gives
\begin{align*}
-
\sum_{i,j=1}^{m}
h_{u(p)}
\bigl(
R^N(du_p(e_i),du_p(e_j))du_p(e_j),
du_p(e_i)
\bigr)
\geq 0.
\end{align*}
This is exactly where the nonpositive curvature of the target is used.[/guided]